B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background
Abstract
This study investigates the rare decay B0 -> K*0 tau+ tau-, which is sensitive to potential violations of lepton flavor universality predicted by the Standard Model. A Monte Carlo simulated dataset containing both signal and the dominant background process B0 -> K*0 D+ D- was used to train and evaluate machine learning classifiers. After feature selection and parameter tuning, two supervised models -- Boosted Decision Trees (BDTs) and Fully Connected Neural Networks (FCNNs) -- were trained. Feature engineering was then applied to enhance classification performance. On the test set, the BDT achieved an AUC of 0.912 +/- 0.000 and an F1-score of 0.828 +/- 0.001, while the FCNN reached an AUC of 0.877 +/- 0.000 and an F1-score of 0.799 +/- 0.001. These results demonstrate that both models can robustly separate signal from background in rare decay searches, supporting their application in future LHCb analyses.
Keywords
Cite
@article{arxiv.2506.19501,
title = {B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background},
author = {Ziyao Xiong and Qixing Deng and Yidan Sun and Junhua Yang},
journal= {arXiv preprint arXiv:2506.19501},
year = {2025}
}
Comments
This submission is being withdrawn because it contains experimental data that I included without obtaining the necessary authorization. The data appears in the Results and Discussion section and was not approved for public release